cartero Thursday, August 13, 2026 · No. 25960
C++

Escape Routes

Making choices and pinning down what you want a thing to do is the art of API design.

Artificial Intelligence

The Generative Genome

Public consternation about a recent Stanford study stems in part from a question it seems to pose: Has AI created new life?

Quantum Computing

A Quantum Leap in Information Science

A joke about entanglement led to a brainstorming session that spurred Brassard and Bennett’s invention of quantum teleportation.

JavaScript and TypeScript

Deno 2.8

PostgreSQL

Shaun Thomas: Why Postgres Lacks Transparent Data Encryption

If you've ever compared database feature matrices, you may have noticed something a bit peculiar. Oracle has Transparent Data Encryption. SQL Server has it. MySQL has it. Even MariaDB has it. But Postgres, which we all consider the best database engine? Conspicuously absent.It’s not that nobody wants TDE. Compliance frameworks like PCI DSS and HIPAA practically demand encryption at rest. Cloud deployments make the “stolen disk” threat model more tangible than ever. And the question come...

Anthropic

The Download: coding’s future, the ‘Steroid Olympics,’ and AI-driven science

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Anthropic’s Code with Claude showed off coding’s future—whether you like it or not At Anthropic’s developer event in London this week, Code with Claude, attendees were asked if they’d shipped code…

Database Migration

Umair Shahid: Long-running transactions, job queues, and the cascade that wreaks havoc

A scheduled PostgreSQL migration step held an open transaction snapshot for hours during the initial data copy. A job queue running at production write velocity began to slow down. Twelve hours later, the queue was seven million rows deep, the primary was pinned at 100 percent CPU across 24 cores, and customer support was fielding complaints about delayed transactions.Staging had validated the migration plan. Every rehearsal was green. And yet here we were.The team had the system stable that ...

Artificial Intelligence

Google I/O showed how the path for AI-driven science is shifting

During Tuesday’s Google I/O keynote, Demis Hassabis, the CEO of Google DeepMind, proclaimed that we are currently “standing in the foothills of the singularity.” It was a striking statement—the singularity is the theoretical future moment when AI rapidly exceeds human intelligence and dramatically transforms the world. But what struck me as I listened in the…

NVIDIA

Breaking your AI storage bottlenecks

Recorded at HumanX, Ryan sits down with Garima Kapoor and Anand Babu Periasamy, co-founders and co-CEOs of MinIO, to chat about eliminating the storage bottlenecks that leave GPUs underutilized, their partnership with NVIDIA on the new STX reference architecture, and why modern AI infrastructure is converging on S3-compatible object storage.

Graph Databases

Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study

arXiv:2605.21510v1 Announce Type: new Abstract: Reference-based graph compression encodes each vertex's neighbor list relative to a recent vertex, exploiting locality to compress large directed graphs. The dominant tool, WebGraph's BVGraph, fixes a single encoding pipeline and relies on a separately chosen vertex ordering -- typically URL-lexicographic or Layered Label Propagation (LLP). The interaction between ordering and encoder is rarely measured. We propose a two-stage Leiden+LLP vert...

Blockchain

Chain Reactions: How Nonce Collisions in ECDSA Compromise Polygon MEV Searchers

arXiv:2605.21498v1 Announce Type: new Abstract: ECDSA signatures form the bedrock of blockchain transaction authentication, yet their security critically depends on proper nonce generation. We uncover a critical vulnerability in the Polygon MEV ecosystem: systematic nonce reuse that enables complete private key recovery. Analyzing on-chain data reveals that searchers, driven by the need for sub-second response times in sealed-bid auctions, employ predictable nonce patterns. These patterns cr...

AI Agents

Autonomous LLM Agents & CTFs: A Second Look

arXiv:2605.21497v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly proposed to automate offensive security tasks, with recent studies reporting near human-level success rates in Capture-the-Flag (CTF) challenges. We here revisit these results, providing a second look at these claims. We engineer different agent architectures of increasing complexity and modularity on 30 web-based CTFs challenges spanning 14 vulnerability classes. We instantiate these agents wi...

Anthropic

HealthCraft: A Reinforcement Learning Safety Environment for Emergency Medicine

arXiv:2605.21496v1 Announce Type: new Abstract: Frontier language models are being deployed into clinical workflows faster than the infrastructure to evaluate them safely. Static medical-QA benchmarks miss the failure modes that matter in emergency medicine: trajectory-level safety collapse, tool misuse, and capitulation under sustained clinical pressure. We present HealthCraft, the first public reinforcement-learning environment that rewards trajectory-level safety under realistic emergency...

Compliance and Privacy

Requirements Perception Gap across Stakeholders: A Comparative Survey of Aged Care Digital Health Software

arXiv:2605.21495v1 Announce Type: new Abstract: We sought to explore and compare the perspectives of three key stakeholder groups: older adults, caregivers (formal health providers and informal caregivers), and digital health software developers on key functional and non-functional requirements. We conducted a survey, designed based on the findings from an existing systematic review, to gather and analyse data related to the three stakeholder groups' (dis)satisfaction with current aged care ...

Machine Learning

Double descent for least-squares interpolation on contaminated data: A simulation study

arXiv:2605.21494v1 Announce Type: new Abstract: Overparametrized models can exhibit an excellent generalization performance, although they should be prone to overfitting according to classical statistical theory. The discovery of the "double descent", indicating that the generalization error decreases after a certain model complexity has been reached, opened a new line of research. Robust statistics considers statistical estimation on contaminated data, which, due to assumptions that do not ...

Artificial Intelligence

Don't Collapse Your Features: Why CenterLoss Hurts OOD Detection and Multi-Scale Mahalanobis Wins

arXiv:2605.21493v1 Announce Type: new Abstract: The ability to detect out-of-distribution (OOD) inputs is fundamental to safe deployment of machine learning systems. Yet, current methods often rely on feature representations that are optimised solely for classification accuracy, neglecting the distinct requirements of epistemic uncertainty. We introduce GOEN (Geometry-Optimised Epistemic Network), a simple pipeline that combines multi-scale features, L2 normalisation, Mahalanobis distance, a...

AGI and AI Safety

The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity

arXiv:2605.21492v1 Announce Type: new Abstract: No feature ranking can be simultaneously faithful, stable, and complete when features are collinear. For collinear pairs, ranking reduces to a coin flip. We prove this impossibility, quantify it for four model classes, resolve it via ensemble averaging (DASH), and machine-verify it with 305 Lean 4 theorems. We characterize the complete attribution design space: exactly two families of methods exist -- faithful-complete methods (unstable, with r...

LLM Evaluation

Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation

arXiv:2605.21491v1 Announce Type: new Abstract: As language models accelerate scientific research by automating hypothesis generation and implementation, a new bottleneck emerges: evaluating and filtering hundreds of AI-generated ideas without exhaustive experimentation. We ask whether LMs can learn to forecast the empirical success of research ideas before any experiments are run. We study comparative empirical forecasting: given a benchmark-specific research goal and two candidate ideas, p...

Artificial Intelligence

Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding

arXiv:2605.21490v1 Announce Type: new Abstract: We introduce the Temporal Contrastive Transformer (TCT), a representation learning framework designed to capture contextual temporal dynamics in sequences of financial transactions. The model is trained using a self-supervised contrastive objective to produce embeddings that encode behavioral patterns over time, with the goal of supporting downstream fraud detection tasks. We evaluate TCT in a realistic setting by using the learned embeddings a...